Can PSMA PET/CT help in dose-tailoring in post-prostatectomy radiotherapy?
Bibliographic record
Abstract
There are few randomized trials to evaluate the use of PSMA-PET in the planning of post-prostatectomy radiotherapy. There are two unresolved questions 1) should we increase the dose to lesions visible on PSMA-PET 2) can we reduce dose in the case of a negative PSMA-PET. In this review, we summarize and discuss the available evidence in the literature. We found that in general, there seems to be an advantage for dose-increase, but ta large recent study from the pre-PSMA era didn't show an advantage for dose escalation. Retrospective studies have shown that conventional doses to PSMA-PET-positive lesions seem sufficient. On the other hand, in the case of a negative PSMA-PET, there is no evidence that dose-reduction is possible. In the future, the combination of PSMA-PET with genomic classifiers could help in better identify patients who might benefit from either dose- de-or -increase. We further need to identify intraindividual references to help identify lesions with higher aggressiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".